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Martin E. MüllerView profile

Professor

• Algebraic Logic
• Modal Logic
• Relational Algebra
• +7 more

Prof. Dr. Martin E. Müller is a Professor in the Department of Computer Science at Bonn-Rhein-Sieg University of Applied Sciences, specializing in the mathematical and theoretical foundations of informatics. His research bridges abstract algebraic structures with practical computational applications, particularly in knowledge representation and reasoning systems. Dr. Müller's primary research interests include Algebraic Logic , Modal Logic , Relational Algebra , Universal Algebra , and Logic Knowledge Discovery (also known as explainable machine learning). His work demonstrates how theoretical mathematical frameworks can provide robust foundations for practical computational problems, particularly in the areas of rough set theory, formal concept analysis, and inductive logic programming. He approaches machine learning through logical structures, emphasizing transparency and explainability in AI systems. Analysis of his publication record shows a consistent scholarly trajectory from 1994 through 2023, with increasing emphasis on applying formal logical methods to contemporary machine learning challenges. His work spans theoretical foundations of computing, relational methods in program semantics, and practical applications in user modeling and knowledge discovery. Recent publications demonstrate growing interest in making machine learning more interpretable through logical frameworks. Among his professional recognitions is the Seahorse award from 1975 . Dr. Müller serves as a reviewer for numerous academic journals and conferences and is an active member of several professional associations in computer science and logic. Dr. Müller has led significant research projects including PARIA (2004-2008), which developed the PACME architecture for concurrent processes; Rela-X (2010-2019), which implemented libraries for efficient relation calculus with the R-Lang programming language; and the ongoing COMPARE project (2024-) focusing on pairwise multidimensional comparisons for survey analysis. His current work with the 'Sets, Structures, Semantics' project (2018-) aims to create a comprehensive resource on discrete mathematics and logics. His research group maintains strong international collaborations, particularly with researchers in the relational and algebraic methods community, including notable figures like Tony Hoare, Peter Höfner, Peter Jipsen, and Bernhard Möller. The Rela-X project established a productive student working group environment that produced both theoretical insights and practical software tools for relational calculus visualization.

• Artificial Intelligence
• Human-Computer Interaction
• Digital Health
• +6 more

Maurice D. Mulvenna is a Researcher at the School of Computing and Mathematics, Ulster University , focusing on Artificial Intelligence, Digital Health, and Human-Computer Interaction . His work explores the application of AI in mental wellbeing, assistive technologies for dementia care, and usability testing methodologies. Research Themes : AI for Wellbeing, Ambient Assisted Living, Machine Learning in Healthcare, IoT for Elderly Care, Sentiment Analysis Recent Articles : 2025 study on AI's impact on mental health; 2024 work on employee wellbeing platforms; 2023 papers on chatbots and IoT lighting solutions for dementia His collaborations span Raymond R. Bond, Siobhan O'Neill, and Chris D. Nugent , with publications in journals like Behavior & Information Technology and conferences such as ICT4AWE and ECCE . While no explicit awards are listed, his contributions include co-editing conference proceedings and advancing ethical-by-design frameworks. Labs/Teams : Involved in projects like SenseCare (2016) for emotional wellbeing visualization and UX-Handle (2017) for usability analytics. His work bridges technical innovation with user-centered approaches in healthcare and digital platforms.

• Pervasive Computing
• Mobile Sensing
• Drone Networks
• +5 more

Huber Flores is a Professor in the Department of Computer Science at Aalto University's School of Science, specializing in pervasive computing, mobile sensing, and sustainable technology applications. His research bridges the gap between theoretical computer science and real-world environmental challenges through innovative applications of drone networks, thermal imaging, and AI systems. His research interests focus on Pervasive Computing , Mobile Sensing , Drone Networks , Environmental Monitoring , AI Applications , and Sustainable Computing . Flores develops systems that leverage everyday interactions and low-cost sensing to address environmental sustainability challenges, particularly in plastic pollution monitoring, urban air quality assessment, and resource optimization. His work on thermal dissipation sensing modalities represents a novel approach to human-environment interaction understanding. Analysis of his recent publications shows a strong trend toward integrating large language models with multi-sensor data for context reasoning, while maintaining focus on practical environmental applications. His research consistently addresses scalability challenges in city-scale autonomous drone deployments and sustainable computing through e-waste repurposing. Flores has received no explicitly mentioned scientific awards in the available literature, though his high publication volume in top-tier venues demonstrates significant recognition within the pervasive computing community. His collaborative work spans multiple international institutions, with frequent co-authorship patterns indicating strong connections with Petteri Nurmi, Sasu Tarkoma, Pan Hui, and Mohan Liyanage. His research has secured funding supporting work on drone networks, environmental monitoring systems, and AI robustness frameworks, though specific grant details aren't provided in the source material. Flores leads research on the SPATIAL architecture for AI trustworthiness, LIZARD for plastic litter monitoring, and SEAGULL for underwater plastics analysis, demonstrating his focus on applying computing to pressing environmental challenges through innovative sensing approaches.

• Computer Science Education
• Data Literacy
• Artificial Intelligence in Education
• +3 more

Elena Spörer is a researcher at the Chair of Computer Science Education at Technische Universität München (TUM) . Her work focuses on data literacy, debugging processes, code quality in K-12 education, and pedagogical approaches to quantum computing. She contributes to curriculum development and teacher training initiatives.

• Planetary Science
• Remote Sensing
• Mars Geology
• +3 more

Dr. Michael Aye is a Postdoc researcher at the Institute of Geological Sciences, Freie Universität Berlin, within the Department of Planetary Science and Remote Sensing. His work focuses on planetary science, particularly Martian polar regions, CO2 jet-driven processes, and remote sensing data analysis. Specializes in Martian seasonal activity, araneiform morphology, and Saturn ring dynamics Develops open-source software tools for planetary data (e.g., PlanetaryPy, SpiceyPy) Contributes to citizen science projects like Planet Four He conducts laboratory experiments to validate Martian polar processes under JPL DUSTIE chamber conditions and creates software solutions for analyzing Cassini UVIS data. His publications span planetary data calibration, machine learning applications, and comparative studies of seasonal phenomena across multiple solar system bodies.

Bernhard SaskeView profile

Researcher

• Virtual Reality
• Augmented Reality
• Product Lifecycle Management
• +3 more

Bernhard Saske serves as a Research Associate at the Chair of Virtual Product Development within Dresden University of Technology, where he has been affiliated since completing his mechanical engineering studies in 2002. Holding a doctorate (Dr.-Ing.) earned in 2008 for research on 'Augmented Reality in Maintenance,' he specializes in virtual and augmented reality applications integrated with product lifecycle management systems. His academic credentials include: Master's degree in Mechanical Engineering from Dresden University of Technology (2002) Doctorate (Dr.-Ing.) from Dresden University of Technology (2008) focusing on Augmented Reality in Maintenance Saske's research centers on digital transformation in engineering workflows, with core expertise in virtual/augmented reality implementation, product lifecycle management (PLM), and model-based systems engineering (MBSE). Recent investigations address generative AI applications in CAD processes, sustainable product development frameworks, and component reuse optimization in industrial plant design. His work consistently bridges theoretical methodologies with practical industry implementation challenges. Analysis of his 2023-2025 publications reveals a strategic shift toward artificial intelligence integration in engineering design, particularly generative models for CAD and computer vision systems. Concurrently, his research maintains strong emphasis on sustainability-driven PLM strategies and MBSE adaptation for circular economy principles, reflecting evolving industry priorities in resource efficiency and digitalization. Scientific Awards: No documented awards, fellowships, or major honors were identified in the source materials. Dr. Saske actively contributes to collaborative research projects within the Chair of Virtual Product Development, frequently co-authoring with Prof. Paetzold-Byhain and interdisciplinary teams. While specific student supervision is unrecorded, his publications indicate mentorship roles in conference papers and journal articles. Project funding appears aligned with German academic-industry partnerships focused on digital engineering solutions, though grant details remain unspecified. As a core member of the Chair of Virtual Product Development, Saske operates within a research ecosystem dedicated to advancing virtual prototyping, digital twin technologies, and immersive maintenance solutions. The chair maintains active industry collaborations to implement VR/AR methodologies in real-world manufacturing and product development contexts across European industrial partners.

Debayan ChatterjeeView profile

Researcher

• Fluvial Geomorphology
• Sedimentary Systems
• Remote Sensing
• +3 more

Debayan Chatterjee is a doctoral researcher (WiMi) at the Institute of Geological Sciences, Freie Universität Berlin, specializing in sedimentary systems. His research focuses on understanding long-term river lateral migration dynamics, floodplain evolution, and valley widening processes through interdisciplinary approaches combining remote sensing, analog modeling, and geospatial data analysis. Education: M.Sc. in Remote Sensing, GeoInformation, and Visualization (Universität Potsdam, 2020–2025); M.Sc. in Geophysics (IIT Kharagpur, 2017–2019); B.Sc. in Physics (St. Xavier’s College Ranchi, 2013–2016) Current Work: Investigates river bank erosion controls using global erosion rate datasets, remote sensing techniques, and landscape evolution experiments Methods: Structure-from-Motion photogrammetry, digital topography analysis, analog modeling He is affiliated with the Tectonics and Sedimentary Systems group at Freie Universität Berlin and contributed to the newly funded TIPSY project in 2025. His technical expertise spans research and teaching assistantships at GFZ Helmholtz Centre for Geosciences and Universität Potsdam, as well as prior project work at India’s National Geophysical Research Institute. Key facilities he engages with include the Biomarker Lab, mineral separation labs, and geospatial software workshops. His research intersects sedimentary geology, geomorphology, and geoinformatics to address fundamental questions about landscape evolution.

Stefan SeegererView profile

Research Fellow

• Computer Science Education
• Artificial Intelligence in Education
• Digital Education
• +3 more

Dr. Stefan Seegerer is an Associate Scientist in the Didactics of Computer Science group at the Department of Mathematics and Computer Science , Free University of Berlin. His work focuses on integrating computer science education with digital literacy and artificial intelligence. Research Projects: ENKIS (AI study programs), TrainDL (Data Literacy teacher training), DigiProMIN (digital professionalization for STEM teachers), and others.

Ralf MetznerView profile

Researcher

• Plant Transport Physiology
• Radiotracer Imaging
• Positron Emission Tomography
• +5 more

Dr. Ralf Metzner serves as Deputy Head of Enabling Technologies and Team Leader of Plant Radiotracers at the Institute of Bio- and Geosciences (IBG-2: Plant Sciences), Jülich Research Centre. His leadership focuses on developing non-invasive imaging methodologies to study plant transport processes, with direct applications in sustainable agriculture and bioeconomy initiatives. Metzner's research centers on plant transport physiology, particularly the demand-driven allocation of photoassimilates between photosynthetic and non-photosynthetic organs. He pioneers the use of short-lived radiotracers (primarily carbon-11) coupled with Positron Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) to visualize three-dimensional carbon dynamics in living plants. This approach overcomes traditional limitations in studying phloem transport, enabling real-time observation of carbon flow under various environmental stresses including drought and disease. Analysis of his recent publications (2022-2025) reveals three dominant research trajectories: development of the phenoPET plant-dedicated scanner system, investigation of carbon allocation patterns in response to biotic/abiotic stresses, and exploration of plant-microbe interactions in the rhizosphere. His work consistently bridges plant physiology, imaging technology, and agricultural science to address climate adaptation challenges. Metzner leads the Plant Radiotracers team that operates specialized imaging infrastructure including the phenoPET scanner and integrated MRI-PET systems. This facility enables non-invasive 3D analysis of root systems in soil environments, supporting Jülich Research Centre's mission to develop sustainable plant production solutions through advanced phenotyping technologies.

• Computational Communication Science
• Algorithmic Society
• Digital Media Effects
• +3 more

Prof. Dr. Pascal Jürgens is a faculty member and Managing Director of the Department II, Media Studies at the University of Trier . His research pioneers Computational Communication Science , focusing on computer-based methods to understand digital societies, algorithmic influence, and media effects modeling. Recent work explores news consumption dynamics, representation in media, and critical analyses of digital structural changes. Research Interests: Development of computational methods for analyzing text, image, and behavioral data Algorithmic curation's impact on public agenda fragmentation Quantitative approaches to media effects and opinion dynamics Challenges in digital journalism and news recommendation systems Longitudinal studies of media representation Contact: juergens@uni-trier.de

Jan MendlingView profile

Professor

• Business Process Management
• Process Mining
• Information Systems
• +3 more

Jan Mendling is a Professor of Process Science at the Department of Computer Science, Humboldt University of Berlin, Germany. He also holds adjunct professorships at Vienna University of Economics and Business, Austria, and serves as a Principle Investigator at the Weizenbaum Institute, Berlin. Additionally, he co-founded Noreja Intelligence GmbH. University: Humboldt University of Berlin Department: Department of Computer Science Other Affiliations: Weizenbaum Institute, Vienna University of Economics and Business His research spans business process management, process mining, information systems, and sustainability compliance. He has authored over 500 publications and co-authored textbooks including Fundamentals of Business Process Management and Wirtschaftsinformatik . Recent research trends focus on timeline-based process discovery, event sequence analysis, and leveraging large language models (LLMs) for business process sustainability. His work integrates temporal analytics, resource analysis, and contextual frameworks for process mining. Scientific Awards: Runner-Up for Best Paper at ICPM 2024 Winner of Best Paper at EdbA 2024 Best Paper at ER 2021 Best Forum Paper at PoEM 2020 Excellence in Teaching Awards (2016, 2017) He leads projects funded by the Einstein Foundation Berlin, BMBF, EU FP7, Horizon 2020, Erasmus+, and FFG. His editorial roles include founding co-Editor-in-Chief of Process Science and memberships on boards like Software and Systems Modeling and Business Process Management Journal .

Sadaf GulshadView profile

Researcher

• Machine Learning
• Computer Vision
• Explainable AI
• +4 more

Sadaf Gulshad is a Postdoctoral Researcher in Machine Learning and Computer Vision at MultixLab, University of Amsterdam. She completed her PhD (2022) in Computer Science at the University of Amsterdam under Prof. Dr. Arnold Smeulders, and holds a Master's degree in Electrical Engineering from KAIST (South Korea, 2017) and a Bachelor's in Electrical and Computer Engineering from COMSATS Institute (Pakistan, 2015). Research Interests: Sadaf focuses on improving AI transparency and robustness through explainable models. Her work addresses challenges like adversarial examples, medical imaging imperfections, and uncertainty estimation in clinical data. Key contributions include hierarchical prototype explainers (HIPE), transform-augmented networks, and attribute-based reasoning frameworks. Recent Publications: Her research spans explainable AI (CVPR 2023), robustness (NeurIPS 2021), medical imaging analysis (Medical Physics 2023), and uncertainty estimation (European Heart Journal 2021). She develops methods that bridge human cognition with machine learning explainability. Teaching & Supervision: Sadaf has served as Teaching Assistant for courses including "Applied Machine Learning" and "Multimedia Analytics" at the University of Amsterdam. She supervised multiple Master's theses on topics like green roof distribution analysis, semantic segmentation, and interpretable representation learning in collaboration with institutions such as UMC Utrecht and VU Medical Center.

• Multimodal Perception
• Perception and Interaction in Social Networks
• Perception and Action with Digital Agents
• +5 more

Markus Huff serves as a Full Professor of Applied Cognitive Psychology and head of the Applied Cognitive Psychology group at the Eberhard Karls University of Tübingen since 2020. He concurrently leads the Perception and Action research group at the Leibniz Institute for Knowledge Media (IWM). His research program investigates perception and action processes within digital environments through a perception-oriented framework that examines how media-mediated content influences human cognition and behavior. Dr. Huff earned his academic foundation in psychology, mathematics, and computer science at the University of Tübingen. Following his psychology degree, he completed his doctorate (Dr. rer. nat.) at the IWM and University of Tübingen with groundbreaking research on verbal influence on visual memory, which earned him the prestigious Leibniz Young Researcher Award in 2007. His academic trajectory includes postdoctoral positions at IWM, University of Tübingen, and Washington University in St. Louis, followed by a junior professorship in general psychology at Tübingen before heading the Department of Research Infrastructures at the German Institute for Adult Education. Professor Huff's research spans four primary interconnected domains. His Multimodal Perception research investigates how humans process and mentally organize media content across various formats including texts, images, comics, and videos. The Perception and Interaction in Social Networks focus examines the complex interplay between attitudes, knowledge, and metacognition during information reception and social interaction in digital spaces. His work on Perception and Action with Digital Agents explores human-AI communication dynamics, while his research on Risk Perception of Artificial Intelligence addresses the psychological dimensions of human responses to emerging digital technologies. Additionally, he conducts significant metascientific research examining policy effectiveness in the research process, with notable findings on research data policies. Analysis of Professor Huff's recent publication record reveals a clear trajectory toward increasingly sophisticated investigations at the intersection of cognitive psychology and digital technology. His work demonstrates methodological rigor across visual perception, audio-visual integration, event comprehension, and the cognitive impacts of digital environments. A particularly noteworthy trend is his expanding focus on AI-related perception and risk assessment, reflecting the growing importance of human-AI interaction in contemporary cognitive science. His research consistently bridges theoretical cognitive frameworks with practical applications in educational technology, health communication, and digital media design. Leibniz Young Researcher Award by the Leibniz Association (2007) Professor Huff directs a multidisciplinary research group comprising members from both the Applied Cognitive Psychology group at the University's Department of Psychology and the Perception and Action group at the Leibniz Institute for Knowledge Media. His research program has secured substantial funding, evidenced by his prolific publication record in high-impact journals spanning psychology, cognitive science, human-computer interaction, and educational technology. His grant portfolio reflects strong interest in understanding human cognition in digital contexts, with particular emphasis on educational applications, health communication, and human-AI interaction. His laboratory work employs advanced experimental methodologies including eye-tracking, behavioral experiments, and computational modeling to investigate how people process information across various media formats and interact with increasingly sophisticated digital agents. The research environment fosters collaboration between cognitive psychologists, computer scientists, and educational researchers, creating a rich interdisciplinary context for advancing understanding of human perception and action in our rapidly evolving digital landscape.

• Computer Science
• Computer Graphics
• 3D Modeling
• +3 more

Raphael Braun is a teacher at the University of Tübingen, affiliated with the Department of Computer Graphics. Holding an M.Sc. degree, his role primarily involves instruction in technical domains related to computer graphics, games, and special effects during the WS 2019/20 semester. His research and teaching interests align with fields such as Computer Science and Visual Computing , particularly focusing on 3D Modeling and Game Development . While no specific publications or scientific awards are detailed in the provided data, his institutional affiliation underscores expertise in applied computational techniques.

Ron KikinisView profile

Professor

• Medical Image Computing
• Image Processing
• Biomedical Engineering
• +3 more

Prof. Ron Kikinis is a Professor of Medical Image Computing at the University of Bremen (Department of Computer Science, Faculty of Mathematics and Computer Science) and holds leadership roles at Fraunhofer MEVIS and Harvard Medical School’s Surgical Planning Laboratory. He also serves as a Professor of Radiology at Harvard Medical School. His academic journey includes a Medicine degree from the University of Zurich (1982) and radiology residency at Zurich University Hospital, complemented by computer vision research at ETH Zurich. His research focuses on medical imaging algorithms, interdisciplinary computational methods in healthcare, and software development for biomedical applications. Notable contributions include pioneering work in virtual endoscopy, surgical planning systems, and open-source platforms like 3D Slicer. He leads multidisciplinary teams addressing complex imaging challenges, with over 316 peer-reviewed publications. Key areas of interest include segmentation, registration, visualization, and high-performance computing in medical imaging. His work bridges clinical needs with technological innovation, emphasizing reproducible AI pipelines and data commons for translational research. He collaborates globally on initiatives such as the NCI Imaging Data Commons and advancing low-cost medical imaging solutions. Prof. Kikinis’ contributions span academic leadership, clinical translation, and software ecosystem development, making him a pivotal figure in medical image computing and translational imaging research.